private void FillLearningAlgorithmsTable(string NeuroNetName, string SelectionName)
{
dgwLA.Rows.Clear();
string[] names = LearningAlgorithmsLibrary.GetAllNamesOfAlgorithms();
for (int i = 0; i < LearningAlgorithmsLibrary.CountAlgorithms; i++)
{
dgwLA.Rows.Add(names[i]);
}
List <string> ls = learningInfo.FindData(NeuroNetName).FindData(SelectionName);
for (int i = 0; i < dgwLA.Rows.Count; i++)
{
string laName = dgwLA.Rows[i].Cells[0].Value.ToString();
string laType = LearningAlgorithmsLibrary.GetNameOfTypeOfAlgoritm(laName);
bool isConsist = false;
foreach (string item in ls)
{
if (String.Compare(item, laType) == 0)
{
isConsist = true;
break;
}
}
if (isConsist)
{
dgwLA.Rows[i].Cells[1].Value = "Обучена";
}
else
{
dgwLA.Rows[i].Cells[1].Value = "Не обучена";
}
}
dgwLA.AutoSizeColumnsMode = DataGridViewAutoSizeColumnsMode.AllCells;
dgwLA.AutoResizeColumns();
}
public List <string> SelectLearningStatistics(string NeuroNetName, string SelectionName)
{
List <string> ls = new List <string>(LearningAlgorithmsLibrary.GetAllNamesOfTypesOfAlgorithms());
List <int> topologies = new List <int>();
connector.ConnectToDB();
SQLiteCommand cmd = new SQLiteCommand(connector.connection);
cmd.CommandText = "SELECT NetTopology.ID FROM NetTopology, NeuroNet WHERE NeuroNetID = NeuroNet.ID AND NeuroNet.NAME = '" + Convert.ToString(NeuroNetName) + "'";
SQLiteDataReader reader = cmd.ExecuteReader();
try
{
while (reader.Read())
{
topologies.Add(Convert.ToInt32(reader[0]));
}
}
catch (SQLiteException ex)
{
MessageBox.Show(ex.Message);
}
reader.Close();
foreach (int item in topologies)
{
cmd.CommandText = "SELECT TypeLA FROM WeightsMatrix, SELECTION " +
"WHERE SELECTIONID = SELECTION.ID AND SELECTION.NAME = '" + SelectionName
+ "' AND NetTopologyID = '" + Convert.ToString(item) + "'";
List <string> bf = new List <string>();
reader = cmd.ExecuteReader();
try
{
while (reader.Read())
{
bf.Add(Convert.ToString(reader[0]));
}
}
catch (SQLiteException ex)
{
MessageBox.Show(ex.Message);
}
reader.Close();
for (int i = 0; i < ls.Count; i++)
{
bool isConsist = false;
foreach (string idBf in bf)
{
if (String.Compare(idBf, ls[i]) == 0)
{
isConsist = true;
break;
}
}
if (isConsist == false)
{
ls.Remove(ls[i]);
i--;
}
}
}
connector.DisconnectFromDB();
return(ls);
}
private void btnUse_Click(object sender, EventArgs e)
{
int countInputNeurons = dbHandler.SelectCountInputParametersInTask(lbTaskSelected.Text);
int countOutputNeurons = 1;
ActivateFunction af = LibraryOfActivateFunctions.
GetActivateFunction(dbHandler.SelectActivateFunctionTypeByNeuroNet(lbNetSelected.Text),
LibraryOfActivateFunctions.GetterParameter.TypeOfActivateFunctionName);
List <double> valuesOfParametersAF = dbHandler.SelectValuesOfParametersOfAF(lbNetSelected.Text);
int k = 0;
foreach (double item in valuesOfParametersAF)
{
af.SetValueOfParameter(k, item);
k++;
}
int countNeurons = dbHandler.SelectCountNeuronsInNet(lbNetSelected.Text);
bool[,] connections = new bool[countNeurons, countNeurons];
double[,] weights = new double[countNeurons, countNeurons];
List <Tuple <int, int, double> > ls = dbHandler.SelectLearnedTopology(lbNetSelected.Text,
lbSelSelected.Text, LearningAlgorithmsLibrary.GetNameOfTypeOfAlgoritm(lbLASelected.Text));
for (int i = 0; i < countNeurons; i++)
{
for (int j = 0; j < countNeurons; j++)
{
connections[i, j] = false;
weights[i, j] = 0.0;
}
}
foreach (Tuple <int, int, double> item in ls)
{
connections[item.Item2, item.Item1] = true;
weights[item.Item2, item.Item1] = item.Item3;
}
int[] neuronsInLayers = dbHandler.SelectNeuronsInLayers(lbNetSelected.Text);
NeuroNet net = new NeuroNet(countInputNeurons, countOutputNeurons, neuronsInLayers, connections, weights, af);
NeuroNetSolvingWindow solvingWnd = new NeuroNetSolvingWindow(net);
solvingWnd.Show();
}
